Conventional versus Monte Carlo SPECT reconstruction of Lu‐177: Toward reduced bias and variance in quantitative imaging
Bibliographic record
Abstract
Abstract Background Monte Carlo (MC)‐based single photon emission computed tomography (SPECT) reconstruction utilizes advanced system models that stochastically sample all possible photon interactions within the patient and detector, potentially increasing quantitative accuracy and precision. Despite this, there have been few studies that have rigorously compared conventional SPECT reconstruction and MC‐based reconstruction for metrics that are pertinent to radiopharmaceutical dosimetry. Purpose This paper aims to compare conventional reconstruction with hybrid‐MC reconstruction and explores accuracy and precision in total activity estimation within various regions of interest in imaging. Methods The MC engine Simulation of Medical Imaging Nuclear Detectors (SIMIND) was integrated with the reconstruction software PyTomography to enable MC‐based reconstruction. The following are explored: (i) elimination of triple energy window (TEW)‐induced bias obtained by using an MC system model and (ii) improvements to precision and effective reductions in required scan time that are attained when using MC‐based reconstruction. The study explores multiple acquisitions of simulated and real phantom/patient data. Results Conventional reconstruction with TEW induces a positive bias (e.g., 116.3% recovery coefficient, or RC, in a 72 mm sphere from the MC‐simulated data) that is not present with MC‐based reconstruction (e.g., 101.3 RC%). This source of positive bias raises RC in smaller spheres, effectively canceling with negative bias incurred from finite resolution; in real phantom and patient data, RCs are lower with MC‐based reconstruction than conventional, suggesting that MC‐based reconstruction is able to remove the additional source of bias caused by TEW. Furthermore, MC‐based reconstruction is able to reduce variability between subsequent acquisitions of the same phantom (e.g., reducing variability by in 10 mm sphere), thus resulting in reconstructed images comparable to those obtained with longer scan times (e.g., by a factor of in the 32 mm sphere). Conclusions Compared to conventional reconstruction that uses TEW to correct for scatter, MC‐based reconstruction for (i) reduces sources of systematic bias caused by inadequate TEW scatter estimation and (ii) reduced required scan times by reducing total uptake variability in regions of interest across different scans. For this reason, MC‐based reconstruction should be preferred to conventional reconstruction in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".